Bring your own data

Day 4 - Introduction to Data Analysis with R

Selina Baldauf

Freie Universität Berlin - Theoretical Ecology

October 14, 2024

Organization

Schedule of today

  • Now - 14.30: Work on the data set(s)

    • Take break(s) as best fits your needs
    • Keep an eye on the chat

  • 14.30 - 15: Short feedback round

    • Show us your favorite plot
    • Did you learn something new/cool you want to share?
    • Was there something you struggled with?
    • …

  • 15-16: Feedback, conclusion

Data set 1: What makes a good wine?

Physicochemical properties of wine and quality judgements

'data.frame':   1599 obs. of  12 variables:
 $ fixed.acidity       : num  12.7 9.8 6.5 8.6 7.5 7.6 10.1 6.4 6.1 6.7 ...
 $ volatile.acidity    : num  0.6 0.66 0.88 0.52 0.58 0.5 0.935 0.4 0.58 0.46 ...
 $ citric.acid         : num  0.49 0.39 0.03 0.38 0.14 0.29 0.22 NA 0.23 0.24 ...
 $ residual.sugar      : num  2.8 3.2 NA 1.5 2.2 2.3 3.4 1.6 2.5 1.7 ...
 $ chlorides           : num  0.075 0.083 0.079 0.096 0.077 NA 0.105 0.066 0.044 0.077 ...
 $ free.sulfur.dioxide : num  5 21 23 5 27 5 11 5 16 18 ...
 $ total.sulfur.dioxide: num  NA 59 47 18 60 NA 86 12 70 34 ...
 $ density             : num  0.999 0.999 0.996 NA 0.996 ...
 $ pH                  : num  3.14 3.37 NA 3.2 3.28 3.32 3.43 3.34 3.46 3.39 ...
 $ sulphates           : num  0.57 0.71 0.5 0.52 0.59 NA 0.64 NA NA 0.6 ...
 $ alcohol             : num  11.4 11.5 11.2 9.4 9.8 11.5 11.3 9.2 12.5 10.6 ...
 $ quality             : int  5 7 4 5 5 6 4 5 6 6 ...

Data set 1: What makes a good wine?

Ideas - know methods

  • Plot of wine quality against chemical properties
  • Plot of distribution of chemical properties

Ideas - new methods

  • Correlation plots: How are the different wine properties correlated with each other?
  • PCA: How are the wine properties related to each other?

Frederik Vandaele - originally posted to Flickr as Château Pétrus, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=5145286

Data set 1: What makes a good wine?

Data set 2: Paralympic games from 1980-2016

Most important variables:

variable class description
gender character Binary gender
event character Event name
medal character Medal type
athlete character Athlete name (LAST NAME first name
abb character Country abbreviation
country character Country name
type character Type of sport
year double year of games

Data set 2: Paralympic games from 1980-2016

  • E.g. Summarize medal counts by country

Data set 4: Ice cover and temperature

Temperature and ice duration on lakes since 19th century

  • 2 data sets with measurements of
    • ice start, end and duration on 2 lakes in Wisconsin
    • daily air temperature since 1870
  • Explore the effect of climate change on ice cover

Image by LTER under CC BY-SA 4.0

Data set 4: Ice cover and temperature

Ice data:

# A tibble: 6 × 5
  lakeid       ice_on     ice_off    ice_duration  year
  <fct>        <date>     <date>            <dbl> <dbl>
1 Lake Mendota NA         1853-04-05           NA  1852
2 Lake Mendota 1853-12-27 NA                   NA  1853
3 Lake Mendota 1855-12-18 1856-04-14          118  1855
4 Lake Mendota 1856-12-06 1857-05-06          151  1856
5 Lake Mendota 1857-11-25 1858-03-26          121  1857
6 Lake Mendota 1858-12-08 1859-03-14           96  1858

Temperature data:

# A tibble: 6 × 3
  sampledate  year ave_air_temp_adjusted
  <date>     <dbl>                 <dbl>
1 1870-06-05  1870                  20  
2 1870-06-06  1870                  18.3
3 1870-06-07  1870                  17.5
4 1870-06-09  1870                  13.3
5 1870-06-10  1870                  13.9
6 1870-06-11  1870                  15

Data set 4: Ice cover and temperature

Data set 5: RNAseq data

  • Data from FU et al. 2015, Nature Cell Biology

  • 3 csv files:

    • heatmap_genes.csv: A list of the names of interesting genes to look at (Genes used in Figure 6b in paper)
    • DE_results.csv: Gene expression in luminal cells in pregnant versus lactating mice
      • logFC, AveExpr, t, p-value
      • Also contains non-significantly expressed genes
    • normalized_counts: Normalized counts for genes for the different samples

Data set 5: RNAseq data

New methods: Correlation plots and PCAS

Some general tips

  • First make a plan:
    • What do you want to achieve and what are the steps?
    • Start with something small, e.g. reading in the data and bringing it into the right format.
    • If you want, stop by in general to discuss your plan or write me in the chat
  • Google, use ChatGPT, …
  • If you get stuck, ask
    • come to the general meeting
    • write in the chat (you can also write a pm directly to me)

Now you

Working with real research data

Meet in your group (if you want)
Work on your data set
Take breaks as you need and be back at 2.30 p.m.
Keep an eye on your group and the general chat

Sharing

In 1-2 mins:

  • What was the highlight of your analysis?

    • Your favorite plot
    • Some cool code
    • A problem that you finally solved
    • Something new you learned

  • What was difficult?

  • If you want: Share a screenshot in the chat or share your screen

Feedback

Please take 10 mins to complete the feedback survey for the Graduate center (don’t use Internet Explorer)

https://votingo.cedis.fu-berlin.de/PCNLP3

Feedback

  • Any other feedback or comments from your side?

Conclusion

We learned a lot of stuff!

Conclusion

How to continue from here?

Artwork by Allison Horst

The End

Thanks a lot for participating!

Artwork by Allison Horst

Selina Baldauf // Bring your own data